Global Head of Data Solutions and Engineering (Hybrid, Copenhagen)

Job summary

This role is essential for shaping the enterprise data foundation, providing architectural direction, operational discipline, and leadership to scale data capabilities and support analytics, AI, and business transformation.

Qualifications

• Over 10 years of technical experience in data engineering and architecture, with a successful history of building enterprise-grade data platforms in cloud environments.
• At least 5 years of leadership experience, particularly in managing distributed engineering teams.
• Strong understanding of AI enablement on data stacks and collaboration with data scientists and AI/ML engineers.
• Knowledge of governance, security, and compliance to ensure data quality and operational reliability.
• Proficient in English as the official company language.

Responsibilities

• Own and evolve the enterprise data platform architecture, ensuring scalability and reliability.
• Develop core data engineering capabilities, including data ingestion frameworks and CI/CD pipelines.
• Translate architectural strategies into reusable engineering components for various business domains.
• Deliver production-ready data products for analytics and AI/ML use cases.
• Collaborate with cross-functional teams to align designs with functional and regulatory requirements.
• Define engineering standards to improve development quality and platform adoption.
• Maintain operational excellence in platform reliability and data privacy.
• Drive continuous optimization of platform performance and cost management.
• Lead and develop high-performing engineering teams across multiple locations.

Skills

• Deep technical expertise in modern data stacks (e.g., Databricks, Fabric), pipeline orchestration, and cloud automation (preferably Azure).
• Strong cross-functional communication skills to convey technical concepts to senior stakeholders.

Education

• Relevant degree in a technical field is preferred but not explicitly stated.

Tools

• Familiarity with modern data tools and technologies, including cloud platforms and data engineering frameworks.